Visual Relationship Detection Based on Guided Proposals and Semantic Knowledge Distillation

A thorough comprehension of image content demands a complex grasp of the interactions that may occur in the natural world. One of the key issues is to describe the visual relationships between objects. When dealing with real world data, capturing these very diverse interactions is a difficult problem. It can be alleviated by incorporating common sense in a network. For this, we propose a framework that makes use of semantic knowledge and estimates the relevance of object pairs during both training and test phases. Extracted from precomputed models and training annotations, this information is distilled into the neural network dedicated to this task. Using this approach, we observe a significant improvement on all classes of Visual Genome, a challenging visual relationship dataset. A 68.5% relative gain on the recall at 100 is directly related to the relevance estimate and a 32.7% gain to the knowledge distillation.

On Deep Representation Learning from Noisy Web Images

The keep-growing content of Web images may be the next important data source to scale up deep neural networks, which recently obtained a great success in the ImageNet classification challenge and related tasks. This prospect, however, has not been validated on convolutional networks (convnet) -- one of best performing deep models -- because of their supervised regime. While unsupervised alternatives are not so good as convnet in generalizing the learned model to new domains, we use convnet to leverage semi-supervised representation learning. Our approach is to use massive amounts of unlabeled and noisy Web images to train convnets as general feature detectors despite challenges coming from data such as high level of mislabeled data, outliers, and data biases. Extensive experiments are conducted at several data scales, different network architectures, and data reranking techniques. The learned representations are evaluated on nine public datasets of various topics. The best results obtained by our convnets, trained on 3.14 million Web images, outperform AlexNet trained on 1.2 million clean images of ILSVRC 2012 and is closing the gap with VGG-16. These prominent results suggest a budget solution to use deep learning in practice and motivate more research in semi-supervised representation learning.